March 21, 2016
複習
每次開始前
- 打開GitHub桌面版
- 打GitHub帳密與Git config資料
- Clone上次交的作業回本機端(存到桌面)
- 提示：左上角的+號，選Clone
- 點兩下Clone回來的資料夾裡面的.Rproj
Debugging
可以執行程式碼
你之前的程式碼沒打完（不知道是哪裡沒打完的話，按Esc砍掉重練）
Debugging 2
- R的錯誤訊息
- Message：有可能的錯誤通知，程式會繼續執行
- Warning：有錯誤，但是不會影響太多，程式還是會繼續執行
- Error：有錯，而且無法繼續執行程式
- Condition：可能會發生的情況
log(-1)
## Warning in log(-1): NaNs produced
## [1] NaN
mena(NA)
## Error in eval(expr, envir, enclos): could not find function "mena"
從上次的解答開始
記得先把NBA14-15的資料讀進來
if (!require('SportsAnalytics')){
install.packages("SportsAnalytics")
library(SportsAnalytics)
}
NBA1415<-fetch_NBAPlayerStatistics("14-15")
for(i in 1:nrow(NBA1415)){
if(NBA1415[i,"GamesPlayed"]>70&NBA1415[i,"TotalPoints"]>1500){
print(NBA1415[i,c("Name","Team","Position")])
}
}
## Name Team Position ## 9 Lamarcu Aldridge POR PF ## Name Team Position ## 112 Stephen Curry GSW PG ## Name Team Position ## 142 Monta Ellis DAL SG ## Name Team Position ## 197 James Harden HOU SG ## Name Team Position ## 231 Kyrie Irving CLE PG ## Name Team Position ## 283 Damian Lillard POR PG ## Name Team Position ## 361 Chris Paul LAC PG ## Name Team Position ## 442 Klay Thompson GSW SG
都說了for不好用
- For迴圈在R執行的效率極慢
- 剛剛的問題：取出打超過70場，總得分超過1500分的球員姓名、所屬隊伍、守備位置。
- 用subset() 一行搞定！
subset(NBA1415,GamesPlayed>70&
TotalPoints>1500)[,c("Name","Team","Position")]
## Name Team Position ## 9 Lamarcu Aldridge POR PF ## 112 Stephen Curry GSW PG ## 142 Monta Ellis DAL SG ## 197 James Harden HOU SG ## 231 Kyrie Irving CLE PG ## 283 Damian Lillard POR PG ## 361 Chris Paul LAC PG ## 442 Klay Thompson GSW SG
都說了for不好用-2
Data Frame的subset [ ] 也可以一行搞定！
NBA1415[NBA1415$GamesPlayed>70&
NBA1415$TotalPoints>1500,
c("Name","Team","Position")]
## Name Team Position ## 9 Lamarcu Aldridge POR PF ## 112 Stephen Curry GSW PG ## 142 Monta Ellis DAL SG ## 197 James Harden HOU SG ## 231 Kyrie Irving CLE PG ## 283 Damian Lillard POR PG ## 361 Chris Paul LAC PG ## 442 Klay Thompson GSW SG
可是….還是有想用for的時候啊….
想起上次上課…馬刺隊得分最高的好像是…
San<-subset(NBA1415,Team=='SAN') order(San$TotalPoints,decreasing = T)
## [1] 7 11 13 9 8 6 4 10 3 14 12 5 2 1 15
San[order(San$TotalPoints,decreasing = T)[1],
c("Name","Team","TotalPoints")]
## Name Team TotalPoints ## 137 Tim Duncan SAN 1070
如果要得到每隊最高分的球員名單呢？
我們還是用for+unique()試試看好了
NBA14-15球季，每隊最高分的球員名單
unique(NBA1415$Team)
## [1] NYK MEM OKL MIN POR NOR PHI IND DAL MIA BRO SAN MIL DET ATL HOU DEN ## [18] CHI GSW LAC PHO BOS WAS UTA SAC CHA LAL TOR CLE ORL ## 30 Levels: ATL BOS BRO CHA CHI CLE DAL DEN DET GSW HOU IND LAC LAL ... WAS
for(team in unique(NBA1415$Team)){
selectTeam<-subset(NBA1415,Team==team)
print(selectTeam[order(selectTeam$TotalPoints,decreasing = T)[1],
c("Name","Team","TotalPoints")])
}
## Name Team TotalPoints ## 19 Carmelo Anthony NYK 966 ## Name Team TotalPoints ## 166 Marc Gasol MEM 1413 ## Name Team TotalPoints ## 470 Russel Westbrook OKL 1886 ## Name Team TotalPoints ## 473 Andrew Wiggins MIN 1387 ## Name Team TotalPoints ## 283 Damian Lillard POR 1720 ## Name Team TotalPoints ## 117 Anthony Davis NOR 1656 ## Name Team TotalPoints ## 105 Robert Covington PHI 927 ## Name Team TotalPoints ## 317 C.j. Miles IND 942 ## Name Team TotalPoints ## 142 Monta Ellis DAL 1513 ## Name Team TotalPoints ## 458 Dwyane Wade MIA 1331 ## Name Team TotalPoints ## 286 Brook Lopez BRO 1236 ## Name Team TotalPoints ## 137 Tim Duncan SAN 1070 ## Name Team TotalPoints ## 316 Khris Middleton MIL 1055 ## Name Team TotalPoints ## 135 Andre Drummond DET 1130 ## Name Team TotalPoints ## 324 Paul Millsap ATL 1218 ## Name Team TotalPoints ## 197 James Harden HOU 2217 ## Name Team TotalPoints ## 274 Ty Lawson DEN 1143 ## Name Team TotalPoints ## 167 Pau Gasol CHI 1446 ## Name Team TotalPoints ## 112 Stephen Curry GSW 1900 ## Name Team TotalPoints ## 361 Chris Paul LAC 1564 ## Name Team TotalPoints ## 52 Eric Bledsoe PHO 1377 ## Name Team TotalPoints ## 436 Isaiah Thomas BOS 1101 ## Name Team TotalPoints ## 462 John Wall WAS 1385 ## Name Team TotalPoints ## 206 Gordon Hayward UTA 1463 ## Name Team TotalPoints ## 168 Rudy Gay SAC 1432 ## Name Team TotalPoints ## 236 Al Jefferson CHA 1080 ## Name Team TotalPoints ## 215 Jordan Hill LAL 841 ## Name Team TotalPoints ## 289 Kyle Lowry TOR 1244 ## Name Team TotalPoints ## 235 Lebron James CLE 1740 ## Name Team TotalPoints ## 457 Nikola Vucevic ORL 1428
用rbind()把輸出整理成一個Data Frame
FinalOutput<-NULL
for(team in unique(NBA1415$Team)){
selectTeam<-subset(NBA1415,Team==team)
FinalOutput<-rbind(FinalOutput,
selectTeam[
order(selectTeam$TotalPoints,decreasing = T)[1],
c("Name","Team","TotalPoints")])
}
FinalOutput
## Name Team TotalPoints ## 19 Carmelo Anthony NYK 966 ## 166 Marc Gasol MEM 1413 ## 470 Russel Westbrook OKL 1886 ## 473 Andrew Wiggins MIN 1387 ## 283 Damian Lillard POR 1720 ## 117 Anthony Davis NOR 1656 ## 105 Robert Covington PHI 927 ## 317 C.j. Miles IND 942 ## 142 Monta Ellis DAL 1513 ## 458 Dwyane Wade MIA 1331 ## 286 Brook Lopez BRO 1236 ## 137 Tim Duncan SAN 1070 ## 316 Khris Middleton MIL 1055 ## 135 Andre Drummond DET 1130 ## 324 Paul Millsap ATL 1218 ## 197 James Harden HOU 2217 ## 274 Ty Lawson DEN 1143 ## 167 Pau Gasol CHI 1446 ## 112 Stephen Curry GSW 1900 ## 361 Chris Paul LAC 1564 ## 52 Eric Bledsoe PHO 1377 ## 436 Isaiah Thomas BOS 1101 ## 462 John Wall WAS 1385 ## 206 Gordon Hayward UTA 1463 ## 168 Rudy Gay SAC 1432 ## 236 Al Jefferson CHA 1080 ## 215 Jordan Hill LAL 841 ## 289 Kyle Lowry TOR 1244 ## 235 Lebron James CLE 1740 ## 457 Nikola Vucevic ORL 1428
問題？
Apply家族介紹
apply()
有類似for迴圈的功能
- apply(Data, MARGIN, FUN,….)
- Data：矩陣（Matrix），Data Frame
- MARGIN：1=row, 2=column
- FUN：函數
- …：函數要用的參數 NBA1415的各列中，最大值是多少
apply(NBA1415,2,max)
## League Name Team ## "NBA" "Zoran Dragic" "WAS" ## Position GamesPlayed TotalMinutesPlayed ## "SG" "83" "2979" ## FieldGoalsMade FieldGoalsAttempted ThreesMade ## "659" "1471" "286" ## ThreesAttempted FreeThrowsMade FreeThrowsAttempted ## "646" "715" "824" ## OffensiveRebounds TotalRebounds Assists ## "437" "1226" "838" ## Steals Turnovers Blocks ## "163" "321" "200" ## PersonalFouls Disqualifications TotalPoints ## "285" "10" "2217" ## Technicals Ejections FlagrantFouls ## "15" "1" "0" ## GamesStarted ## "82"
apply()範例2
NBA1415球季，出場數、出場總分鐘數、總分的最大值各是多少
apply(NBA1415[,c("GamesPlayed","TotalMinutesPlayed","TotalPoints")]
,2,mean)
## GamesPlayed TotalMinutesPlayed TotalPoints ## 52.81707 1210.46341 499.95935
未來用得上的apply()快速功能
- rowSums = apply(x, 1, sum)
- rowMeans = apply(x, 1, mean)
- colSums = apply(x, 2, sum)
- colMeans = apply(x, 2, mean)
sapply(): Simplify
- 如果回傳的list裡面，每個欄位長度都是1，回傳向量
- 如果回傳的list裡面，每個欄位長度都一樣，回傳矩陣
- sapply(Data, FUN…)
- Data：矩陣（Matrix），Data Frame，List，向量
- FUN：函數
- …：函數要用的參數
sapply(iris, mean)
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical: ## returning NA
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 5.843333 3.057333 3.758000 1.199333 NA
sapply()範例2
NBA1415球季，出場數、出場總分鐘數、總分的平均值各是多少
sapply(NBA1415[,c("GamesPlayed","TotalMinutesPlayed","TotalPoints")],
mean)
## GamesPlayed TotalMinutesPlayed TotalPoints ## 52.81707 1210.46341 499.95935
等同於
apply(NBA1415[,c("GamesPlayed","TotalMinutesPlayed","TotalPoints")]
,2,mean)
## GamesPlayed TotalMinutesPlayed TotalPoints ## 52.81707 1210.46341 499.95935
lapply(): List
- For List
- C code
- lapply(Data, FUN…)
- Data：矩陣（Matrix），Data Frame，List
- FUN：函數
- …：函數要用的參數
lapply(iris, mean)
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical: ## returning NA
## $Sepal.Length ## [1] 5.843333 ## ## $Sepal.Width ## [1] 3.057333 ## ## $Petal.Length ## [1] 3.758 ## ## $Petal.Width ## [1] 1.199333 ## ## $Species ## [1] NA
lapply()範例2
NBA1415球季，出場數、出場總分鐘數、總分的平均值各是多少
lapply(NBA1415[,c("GamesPlayed","TotalMinutesPlayed","TotalPoints")],
mean)
## $GamesPlayed ## [1] 52.81707 ## ## $TotalMinutesPlayed ## [1] 1210.463 ## ## $TotalPoints ## [1] 499.9593
tapply()
向量分群後計算
- tapply(Data, INDEX, FUN,…)
- Data：向量
- INDEX：分類因子
- FUN：函數
- …：函數要用的參數
tapply(NBA1415$Name,NBA1415$Team,length)
## ATL BOS BRO CHA CHI CLE DAL DEN DET GSW HOU IND LAC LAL MEM MIA MIL MIN ## 15 15 17 16 14 16 16 15 15 15 16 15 19 18 17 18 17 20 ## NOR NYK OKL ORL PHI PHO POR SAC SAN TOR UTA WAS ## 19 16 16 15 19 16 16 17 15 15 17 17
split(), tapply()的朋友
有tapply()的部分功能：替向量做分群
1到30的向量，用第二個參數做分群
split(1:30,gl(3, 10))
## $`1` ## [1] 1 2 3 4 5 6 7 8 9 10 ## ## $`2` ## [1] 11 12 13 14 15 16 17 18 19 20 ## ## $`3` ## [1] 21 22 23 24 25 26 27 28 29 30
輸出結果是List—>應該要想到lapply()
split()+lapply()
split()分群後的向量，用lapply()做運算
lapply(split(1:30,gl(3, 10)),mean)
## $`1` ## [1] 5.5 ## ## $`2` ## [1] 15.5 ## ## $`3` ## [1] 25.5
效果等同於
tapply(1:30,gl(3, 10),mean)
## 1 2 3 ## 5.5 15.5 25.5
Data Frame也可以split()
替Data Frame做分群
- split(Data, 分群依據)
NBA1415Team<-split(NBA1415[,c("TotalPoints","GamesPlayed")],
NBA1415$Team)
NBA1415Team
## $ATL ## TotalPoints GamesPlayed ## 21 356 63 ## 40 390 75 ## 61 96 36 ## 84 883 71 ## 121 129 35 ## 222 1156 76 ## 239 135 24 ## 266 911 75 ## 292 299 55 ## 324 1218 73 ## 338 197 40 ## 401 768 77 ## 403 529 68 ## 404 278 52 ## 431 1162 73 ## ## $BOS ## TotalPoints GamesPlayed ## 36 866 82 ## 60 1071 77 ## 108 628 82 ## 115 105 21 ## 241 448 75 ## 352 656 64 ## 372 177 50 ## 379 24 21 ## 412 523 67 ## 429 770 58 ## 436 1101 67 ## 449 779 82 ## 463 35 32 ## 488 105 31 ## 492 833 82 ## ## $BRO ## TotalPoints GamesPlayed ## 15 543 74 ## 54 700 78 ## 66 216 47 ## 93 20 9 ## 116 142 27 ## 232 957 80 ## 237 183 50 ## 246 1154 80 ## 255 138 44 ## 259 153 33 ## 263 3 7 ## 286 1236 72 ## 330 81 37 ## 367 717 82 ## 432 339 40 ## 475 886 68 ## 490 1069 76 ## ## $CHA ## TotalPoints GamesPlayed ## 48 304 64 ## 114 176 47 ## 192 254 45 ## 208 969 80 ## 236 1080 65 ## 261 598 55 ## 301 203 61 ## 356 41 9 ## 387 480 72 ## 423 501 61 ## 430 127 29 ## 456 83 25 ## 461 1075 62 ## 479 579 78 ## 480 964 68 ## 491 472 62 ## ## $CHI ## TotalPoints GamesPlayed ## 28 10 18 ## 63 954 82 ## 74 1301 65 ## 138 595 63 ## 167 1446 78 ## 171 642 62 ## 217 377 66 ## 308 109 36 ## 325 833 82 ## 326 25 23 ## 328 149 56 ## 343 485 67 ## 392 904 51 ## 419 435 72 ## ## $CLE ## TotalPoints GamesPlayed ## 91 15 8 ## 123 305 67 ## 201 136 50 ## 207 35 22 ## 231 1622 75 ## 235 1740 69 ## 251 244 57 ## 264 4 5 ## 288 1238 76 ## 295 278 58 ## 320 109 52 ## 335 792 81 ## 407 488 62 ## 415 844 70 ## 443 691 82 ## 453 255 26 ## ## $DAL ## TotalPoints GamesPlayed ## 12 412 74 ## 30 580 77 ## 89 771 75 ## 142 1513 80 ## 153 108 29 ## 199 665 76 ## 234 44 16 ## 238 432 74 ## 347 1333 77 ## 359 1037 66 ## 365 242 68 ## 371 90 29 ## 391 608 68 ## 413 80 42 ## 427 680 59 ## 455 403 64 ## ## $DEN ## TotalPoints GamesPlayed ## 23 382 58 ## 35 397 58 ## 90 1085 78 ## 94 57 30 ## 150 946 75 ## 156 435 51 ## 157 3 3 ## 162 734 59 ## 185 146 43 ## 200 188 55 ## 212 552 73 ## 272 93 24 ## 274 1143 75 ## 341 523 63 ## 348 426 62 ## ## $DET ## TotalPoints GamesPlayed ## 20 87 49 ## 73 460 78 ## 81 1043 82 ## 128 145 34 ## 135 1130 82 ## 233 1117 77 ## 240 632 41 ## 290 98 21 ## 297 36 23 ## 314 663 60 ## 321 29 10 ## 327 1098 69 ## 376 434 58 ## 445 482 76 ## 482 341 63 ## ## $GSW ## TotalPoints GamesPlayed ## 29 467 66 ## 32 827 82 ## 55 422 67 ## 112 1900 80 ## 149 201 46 ## 184 921 79 ## 219 254 59 ## 228 604 77 ## 268 20 16 ## 277 388 49 ## 285 461 78 ## 304 62 15 ## 396 30 33 ## 420 791 76 ## 442 1668 77 ## ## $HOU ## TotalPoints GamesPlayed ## 22 1048 82 ## 46 568 56 ## 62 917 80 ## 83 32 12 ## 129 184 69 ## 164 45 14 ## 197 2217 81 ## 223 646 41 ## 247 74 28 ## 253 386 33 ## 307 487 62 ## 334 855 71 ## 355 182 43 ## 375 274 67 ## 417 1027 83 ## 435 541 77 ## ## $IND ## TotalPoints GamesPlayed ## 10 317 63 ## 102 309 50 ## 170 53 6 ## 211 802 76 ## 214 692 43 ## 216 729 82 ## 293 263 61 ## 317 942 70 ## 395 323 68 ## 402 761 81 ## 411 393 53 ## 428 896 71 ## 465 570 57 ## 469 769 66 ## 472 57 20 ## ## $LAC ## TotalPoints GamesPlayed ## 33 764 76 ## 107 1010 64 ## 110 35 19 ## 119 294 74 ## 131 19 12 ## 151 167 36 ## 190 1469 67 ## 193 38 14 ## 204 425 73 ## 224 18 5 ## 250 21 33 ## 254 946 82 ## 361 1564 82 ## 381 1277 78 ## 385 530 76 ## 389 238 42 ## 448 232 62 ## 450 29 33 ## 474 41 21 ## ## $LAL ## TotalPoints GamesPlayed ## 49 379 63 ## 53 22 2 ## 58 836 71 ## 64 227 19 ## 68 782 35 ## 76 52 6 ## 95 703 59 ## 118 656 79 ## 141 650 65 ## 209 20 9 ## 215 841 70 ## 249 753 76 ## 260 332 52 ## 284 832 74 ## 374 221 43 ## 378 2 1 ## 397 307 67 ## 489 563 42 ## ## $MEM ## TotalPoints GamesPlayed ## 2 94 30 ## 11 539 63 ## 79 243 58 ## 85 384 66 ## 100 1107 70 ## 166 1413 81 ## 187 62 24 ## 188 1168 78 ## 267 419 81 ## 276 777 77 ## 282 286 63 ## 291 0 1 ## 380 1143 71 ## 418 30 12 ## 426 57 19 ## 439 4 2 ## 451 605 79 ## ## $MIA ## TotalPoints GamesPlayed ## 14 320 60 ## 42 212 24 ## 59 928 44 ## 67 20 5 ## 88 813 80 ## 120 3 4 ## 124 1007 72 ## 132 1275 78 ## 133 28 16 ## 143 312 62 ## 181 188 31 ## 203 261 62 ## 248 190 32 ## 313 72 17 ## 339 261 51 ## 458 1331 62 ## 460 175 24 ## 471 564 48 ## ## $MIL ## TotalPoints GamesPlayed ## 18 1030 81 ## 38 598 77 ## 86 966 67 ## 136 518 72 ## 144 121 33 ## 191 53 20 ## 210 471 67 ## 229 669 58 ## 296 118 28 ## 298 20 11 ## 302 806 71 ## 316 1055 79 ## 349 100 34 ## 354 606 73 ## 357 308 25 ## 368 294 73 ## 400 196 27 ## ## $MIN ## TotalPoints GamesPlayed ## 4 60 17 ## 45 298 57 ## 65 122 29 ## 69 457 67 ## 127 710 73 ## 165 323 47 ## 194 219 41 ## 225 198 45 ## 262 22 4 ## 273 778 77 ## 299 781 39 ## 336 512 38 ## 340 543 54 ## 353 27 6 ## 362 213 32 ## 364 386 31 ## 377 8 5 ## 394 226 22 ## 447 0 2 ## 473 1387 82 ## ## $NOR ## TotalPoints GamesPlayed ## 6 443 68 ## 17 833 61 ## 24 557 77 ## 27 256 63 ## 97 573 75 ## 109 340 66 ## 117 1656 68 ## 130 51 12 ## 147 1313 79 ## 159 178 50 ## 179 818 61 ## 218 592 40 ## 315 6 4 ## 319 2 5 ## 369 538 75 ## 398 42 21 ## 477 37 13 ## 483 98 37 ## 484 42 21 ## ## $NYK ## TotalPoints GamesPlayed ## 1 398 68 ## 8 338 61 ## 13 259 53 ## 19 966 40 ## 31 430 29 ## 80 382 42 ## 113 128 32 ## 140 210 39 ## 163 533 45 ## 196 804 70 ## 271 470 76 ## 275 90 17 ## 408 434 42 ## 416 656 82 ## 437 443 62 ## 467 199 51 ## ## $OKL ## TotalPoints GamesPlayed ## 3 537 70 ## 25 777 82 ## 99 273 66 ## 139 686 27 ## 227 917 64 ## 252 187 43 ## 258 1163 75 ## 269 295 47 ## 309 201 32 ## 333 790 74 ## 346 63 35 ## 386 228 68 ## 410 480 80 ## 433 131 16 ## 459 940 80 ## 470 1886 67 ## ## $ORL ## TotalPoints GamesPlayed ## 122 216 59 ## 155 698 58 ## 161 549 75 ## 176 243 47 ## 177 349 56 ## 189 306 52 ## 198 158 45 ## 202 1164 68 ## 294 37 16 ## 342 194 40 ## 350 294 51 ## 351 1292 72 ## 363 731 82 ## 384 188 47 ## 457 1428 74 ## ## $PHI ## TotalPoints GamesPlayed ## 7 93 41 ## 82 432 48 ## 105 927 69 ## 134 46 12 ## 178 17 9 ## 182 411 65 ## 278 0 1 ## 303 660 67 ## 310 106 23 ## 344 744 75 ## 383 172 19 ## 388 73 35 ## 390 308 54 ## 399 386 74 ## 409 586 73 ## 414 338 55 ## 438 45 17 ## 440 626 71 ## 487 507 30 ## ## $PHO ## TotalPoints GamesPlayed ## 34 32 16 ## 52 1377 81 ## 70 69 36 ## 111 0 2 ## 175 231 41 ## 186 884 74 ## 265 1070 63 ## 279 432 69 ## 312 9 6 ## 331 845 81 ## 332 1258 82 ## 373 131 25 ## 444 380 48 ## 446 713 78 ## 464 245 40 ## 485 544 75 ## ## $POR ## TotalPoints GamesPlayed ## 5 1035 78 ## 9 1661 71 ## 37 664 71 ## 51 350 81 ## 96 24 10 ## 106 168 51 ## 158 57 11 ## 160 169 48 ## 169 241 54 ## 257 638 74 ## 281 327 55 ## 283 1720 82 ## 287 566 59 ## 300 956 60 ## 306 424 62 ## 486 219 48 ## ## $SAC ## TotalPoints GamesPlayed ## 47 2 3 ## 87 593 67 ## 98 725 45 ## 104 1421 59 ## 146 176 47 ## 168 1432 68 ## 220 137 46 ## 270 503 70 ## 305 503 68 ## 311 996 82 ## 318 355 81 ## 329 2 3 ## 422 319 73 ## 425 8 3 ## 441 491 81 ## 466 0 2 ## 476 617 74 ## ## $SAN ## TotalPoints GamesPlayed ## 16 74 33 ## 26 137 51 ## 39 461 70 ## 43 568 62 ## 56 264 72 ## 126 708 81 ## 137 1070 77 ## 172 738 70 ## 183 946 81 ## 256 535 79 ## 280 1057 64 ## 322 351 51 ## 358 976 68 ## 421 428 52 ## 481 37 20 ## ## $TOR ## TotalPoints GamesPlayed ## 78 10 8 ## 125 1204 60 ## 154 46 26 ## 195 270 74 ## 205 50 29 ## 243 694 75 ## 245 554 70 ## 289 1244 70 ## 345 4 4 ## 360 648 81 ## 393 807 82 ## 424 14 17 ## 452 963 80 ## 454 775 82 ## 478 1242 80 ## ## $UTA ## TotalPoints GamesPlayed ## 44 0 2 ## 57 567 79 ## 71 973 76 ## 72 374 27 ## 92 6 4 ## 101 27 16 ## 103 80 16 ## 145 90 38 ## 148 393 82 ## 152 1187 74 ## 173 686 82 ## 206 1463 76 ## 221 433 50 ## 230 396 79 ## 242 16 8 ## 244 166 29 ## 323 248 47 ## ## $WAS ## TotalPoints GamesPlayed ## 41 964 63 ## 50 56 29 ## 75 580 75 ## 77 22 7 ## 174 277 51 ## 180 1001 82 ## 213 737 68 ## 226 509 64 ## 337 6 5 ## 366 868 73 ## 370 445 74 ## 382 11 5 ## 405 520 79 ## 406 402 64 ## 434 204 52 ## 462 1385 79 ## 468 106 32
split()搭配apply()家族使用
lapply(NBA1415Team, colMeans) #回傳List
## $ATL ## TotalPoints GamesPlayed ## 567.13333 59.53333 ## ## $BOS ## TotalPoints GamesPlayed ## 541.4 59.4 ## ## $BRO ## TotalPoints GamesPlayed ## 502.17647 53.17647 ## ## $CHA ## TotalPoints GamesPlayed ## 494.1250 55.1875 ## ## $CHI ## TotalPoints GamesPlayed ## 590.35714 58.64286 ## ## $CLE ## TotalPoints GamesPlayed ## 549.75 53.75 ## ## $DAL ## TotalPoints GamesPlayed ## 562.375 60.875 ## ## $DEN ## TotalPoints GamesPlayed ## 474.0 53.8 ## ## $DET ## TotalPoints GamesPlayed ## 519.66667 54.86667 ## ## $GSW ## TotalPoints GamesPlayed ## 601.0667 60.0000 ## ## $HOU ## TotalPoints GamesPlayed ## 592.6875 56.1875 ## ## $IND ## TotalPoints GamesPlayed ## 525.0667 57.8000 ## ## $LAC ## TotalPoints GamesPlayed ## 479.84211 49.94737 ## ## $LAL ## TotalPoints GamesPlayed ## 454.33333 46.27778 ## ## $MEM ## TotalPoints GamesPlayed ## 490.05882 51.47059 ## ## $MIA ## TotalPoints GamesPlayed ## 442.22222 42.88889 ## ## $MIL ## TotalPoints GamesPlayed ## 466.41176 52.70588 ## ## $MIN ## TotalPoints GamesPlayed ## 363.5 38.4 ## ## $NOR ## TotalPoints GamesPlayed ## 440.78947 47.15789 ## ## $NYK ## TotalPoints GamesPlayed ## 421.2500 50.5625 ## ## $OKL ## TotalPoints GamesPlayed ## 597.125 57.875 ## ## $ORL ## TotalPoints GamesPlayed ## 523.13333 56.13333 ## ## $PHI ## TotalPoints GamesPlayed ## 340.89474 44.10526 ## ## $PHO ## TotalPoints GamesPlayed ## 513.7500 51.0625 ## ## $POR ## TotalPoints GamesPlayed ## 576.1875 57.1875 ## ## $SAC ## TotalPoints GamesPlayed ## 487.05882 51.29412 ## ## $SAN ## TotalPoints GamesPlayed ## 556.66667 62.06667 ## ## $TOR ## TotalPoints GamesPlayed ## 568.33333 55.86667 ## ## $UTA ## TotalPoints GamesPlayed ## 417.94118 46.17647 ## ## $WAS ## TotalPoints GamesPlayed ## 476.05882 53.05882
sapply(NBA1415Team, colMeans) #回傳Data frame
## ATL BOS BRO CHA CHI CLE DAL ## TotalPoints 567.13333 541.4 502.17647 494.1250 590.35714 549.75 562.375 ## GamesPlayed 59.53333 59.4 53.17647 55.1875 58.64286 53.75 60.875 ## DEN DET GSW HOU IND LAC LAL ## TotalPoints 474.0 519.66667 601.0667 592.6875 525.0667 479.84211 454.33333 ## GamesPlayed 53.8 54.86667 60.0000 56.1875 57.8000 49.94737 46.27778 ## MEM MIA MIL MIN NOR NYK OKL ## TotalPoints 490.05882 442.22222 466.41176 363.5 440.78947 421.2500 597.125 ## GamesPlayed 51.47059 42.88889 52.70588 38.4 47.15789 50.5625 57.875 ## ORL PHI PHO POR SAC SAN ## TotalPoints 523.13333 340.89474 513.7500 576.1875 487.05882 556.66667 ## GamesPlayed 56.13333 44.10526 51.0625 57.1875 51.29412 62.06667 ## TOR UTA WAS ## TotalPoints 568.33333 417.94118 476.05882 ## GamesPlayed 55.86667 46.17647 53.05882
split()：用多個欄位來分群
想要同時針對隊伍和守備位置做分群呢？
用list將分群依據包起來
- split(資料, list(分群依據1,分群依據2))
NBA1415TP<-split(NBA1415[,c("TotalPoints","GamesPlayed")],
list(NBA1415$Team,NBA1415$Position))
sapply(NBA1415TP, colMeans)#一樣可以用apply家族計算平均值
## ATL.C BOS.C BRO.C CHA.C CHI.C CLE.C DAL.C DEN.C DET.C ## TotalPoints NaN 744.5 687 618.66667 255 271.5 352.3333 489.0 608.5 ## GamesPlayed NaN 73.0 58 63.66667 45 33.5 53.0000 67.5 65.5 ## GSW.C HOU.C IND.C LAC.C LAL.C MEM.C MIA.C MIL.C MIN.C ## TotalPoints 214.3333 339.0 532.5 946 307 916 442 391.75 264.6 ## GamesPlayed 43.0000 26.5 68.5 82 67 81 54 60.00 30.4 ## NOR.C NYK.C OKL.C ORL.C PHI.C PHO.C POR.C SAC.C SAN.C ## TotalPoints 366.00000 388 850.0 822.0 346 432 425 520 NaN ## GamesPlayed 60.66667 51 72.5 66.5 48 69 59 36 NaN ## TOR.C UTA.C WAS.C ATL.PF BOS.PF BRO.PF CHA.PF ## TotalPoints 257.75 686 525.66667 592.00000 527 280.2 288.33333 ## GamesPlayed 32.50 82 63.33333 59.33333 59 41.6 54.66667 ## CHI.PF CLE.PF DAL.PF DEN.PF DET.PF GSW.PF HOU.PF IND.PF ## TotalPoints 732.75 964.5 624.0 473.66667 536.33333 589.5 613 476.0 ## GamesPlayed 60.00 79.0 60.5 52.33333 51.66667 62.5 64 57.5 ## LAC.PF LAL.PF MEM.PF MIA.PF MIL.PF MIN.PF NOR.PF NYK.PF ## TotalPoints 554.25 507.6667 310.4 368.25 263.00000 184.2 943 259 ## GamesPlayed 61.75 56.0000 35.8 36.75 34.33333 31.8 65 53 ## OKL.PF ORL.PF PHI.PF PHO.PF POR.PF SAC.PF SAN.PF ## TotalPoints 463.6667 320.00 265.5 669.75 842.5 298.16667 511.33333 ## GamesPlayed 54.0000 53.25 45.0 63.50 40.5 46.16667 67.16667 ## TOR.PF UTA.PF WAS.PF ATL.PG BOS.PG BRO.PG CHA.PG ## TotalPoints 541.5 442.50 507.6667 743.00000 718.00 641.33333 640.00 ## GamesPlayed 75.0 40.75 61.0000 68.33333 65.25 61.66667 52.75 ## CHI.PG CLE.PG DAL.PG DEN.PG DET.PG GSW.PG HOU.PG IND.PG ## TotalPoints 929.0 818.5 490.25 604.00000 498.00 1180.5 421.0 637.75 ## GamesPlayed 66.5 41.5 62.50 60.33333 43.25 79.0 61.5 56.00 ## LAC.PG LAL.PG MEM.PG MIA.PG MIL.PG MIN.PG NOR.PG NYK.PG ## TotalPoints 497.50 452.0 435.5 364.66667 371.2 417.25 240.33333 454.75 ## GamesPlayed 48.25 45.5 40.5 45.33333 45.0 45.50 33.66667 51.25 ## OKL.PG ORL.PG PHI.PG PHO.PG POR.PG SAC.PG SAN.PG TOR.PG ## TotalPoints 931.3333 459.5 272.00000 644.50 709 397.75 620.6667 1009.5 ## GamesPlayed 55.0000 64.5 38.33333 42.75 58 49.25 66.0000 76.0 ## UTA.PG WAS.PG ATL.SF BOS.SF BRO.SF CHA.SF CHI.SF ## TotalPoints 475.66667 603 506 256 590.66667 362.5 379.6667 ## GamesPlayed 39.66667 50 53 45 53.66667 42.0 57.0000 ## CLE.SF DAL.SF DEN.SF DET.SF GSW.SF HOU.SF IND.SF LAC.SF ## TotalPoints 592.75 492.75 909.5 317.75 874.0 549.5 353.5 498 ## GamesPlayed 59.00 60.75 68.5 55.50 80.5 61.5 51.5 69 ## LAL.SF MEM.SF MIA.SF MIL.SF MIN.SF NOR.SF NYK.SF OKL.SF ## TotalPoints 445.33333 1168 502.3333 681.5 389 209.5 443.2 354.00 ## GamesPlayed 42.33333 78 55.0000 52.0 50 41.0 52.0 46.25 ## ORL.SF PHI.SF PHO.SF POR.SF SAC.SF SAN.SF TOR.SF UTA.SF ## TotalPoints 661.0 517.75 479 374.66667 1012.5 547 408.5 428.4 ## GamesPlayed 56.5 59.00 59 57.66667 67.5 42 45.0 47.6 ## WAS.SF ATL.SG BOS.SG BRO.SG CHA.SG CHI.SG CLE.SG DAL.SG ## TotalPoints 401.0 428.5 442.0 516.5 475.00 609.00000 443.25 1513 ## GamesPlayed 51.8 56.5 56.5 58.5 58.25 62.33333 62.25 80 ## DEN.SG DET.SG GSW.SG HOU.SG IND.SG LAC.SG LAL.SG MEM.SG ## TotalPoints 216.0 853 514.1667 735.33333 942 371.000 420.25 407.4 ## GamesPlayed 39.4 71 54.5000 57.33333 70 36.125 30.25 58.8 ## MIA.SG MIL.SG MIN.SG NOR.SG NYK.SG OKL.SG ORL.SG ## TotalPoints 500.3333 784.66667 730.00000 722.6667 447.0 563.25 536.4 ## GamesPlayed 36.0000 74.66667 41.66667 51.0000 43.5 67.25 50.8 ## PHI.SG PHO.SG POR.SG SAC.SG SAN.SG TOR.SG UTA.SG WAS.SG ## TotalPoints 326.25 314.6 645.75 657.5 581.5 830.66667 270.00 393 ## GamesPlayed 30.25 41.0 62.75 77.5 61.5 55.33333 45.75 40
apply家族的使用時機
- apply：想要apply一個函數功能到陣列（Matrix）的行或列。不太建議在data frame使用
- lapply：想要apply一個函數功能到List中，回傳List
- sapply：同上，但想要回傳的東西是向量（Vector）
- tapply：想要對一個向量分群，分群後apply一個函數功能
類似apply家族的函數：aggregate()
aggregate(資料, by=分組依據, FUN=函數功能)
aggregate(NBA1415$TotalPoints,
by=list(NBA1415$Team,NBA1415$Position),
FUN=mean)
## Group.1 Group.2 x ## 1 BOS C 744.5000 ## 2 BRO C 687.0000 ## 3 CHA C 618.6667 ## 4 CHI C 255.0000 ## 5 CLE C 271.5000 ## 6 DAL C 352.3333 ## 7 DEN C 489.0000 ## 8 DET C 608.5000 ## 9 GSW C 214.3333 ## 10 HOU C 339.0000 ## 11 IND C 532.5000 ## 12 LAC C 946.0000 ## 13 LAL C 307.0000 ## 14 MEM C 916.0000 ## 15 MIA C 442.0000 ## 16 MIL C 391.7500 ## 17 MIN C 264.6000 ## 18 NOR C 366.0000 ## 19 NYK C 388.0000 ## 20 OKL C 850.0000 ## 21 ORL C 822.0000 ## 22 PHI C 346.0000 ## 23 PHO C 432.0000 ## 24 POR C 425.0000 ## 25 SAC C 520.0000 ## 26 TOR C 257.7500 ## 27 UTA C 686.0000 ## 28 WAS C 525.6667 ## 29 ATL PF 592.0000 ## 30 BOS PF 527.0000 ## 31 BRO PF 280.2000 ## 32 CHA PF 288.3333 ## 33 CHI PF 732.7500 ## 34 CLE PF 964.5000 ## 35 DAL PF 624.0000 ## 36 DEN PF 473.6667 ## 37 DET PF 536.3333 ## 38 GSW PF 589.5000 ## 39 HOU PF 613.0000 ## 40 IND PF 476.0000 ## 41 LAC PF 554.2500 ## 42 LAL PF 507.6667 ## 43 MEM PF 310.4000 ## 44 MIA PF 368.2500 ## 45 MIL PF 263.0000 ## 46 MIN PF 184.2000 ## 47 NOR PF 943.0000 ## 48 NYK PF 259.0000 ## 49 OKL PF 463.6667 ## 50 ORL PF 320.0000 ## 51 PHI PF 265.5000 ## 52 PHO PF 669.7500 ## 53 POR PF 842.5000 ## 54 SAC PF 298.1667 ## 55 SAN PF 511.3333 ## 56 TOR PF 541.5000 ## 57 UTA PF 442.5000 ## 58 WAS PF 507.6667 ## 59 ATL PG 743.0000 ## 60 BOS PG 718.0000 ## 61 BRO PG 641.3333 ## 62 CHA PG 640.0000 ## 63 CHI PG 929.0000 ## 64 CLE PG 818.5000 ## 65 DAL PG 490.2500 ## 66 DEN PG 604.0000 ## 67 DET PG 498.0000 ## 68 GSW PG 1180.5000 ## 69 HOU PG 421.0000 ## 70 IND PG 637.7500 ## 71 LAC PG 497.5000 ## 72 LAL PG 452.0000 ## 73 MEM PG 435.5000 ## 74 MIA PG 364.6667 ## 75 MIL PG 371.2000 ## 76 MIN PG 417.2500 ## 77 NOR PG 240.3333 ## 78 NYK PG 454.7500 ## 79 OKL PG 931.3333 ## 80 ORL PG 459.5000 ## 81 PHI PG 272.0000 ## 82 PHO PG 644.5000 ## 83 POR PG 709.0000 ## 84 SAC PG 397.7500 ## 85 SAN PG 620.6667 ## 86 TOR PG 1009.5000 ## 87 UTA PG 475.6667 ## 88 WAS PG 603.0000 ## 89 ATL SF 506.0000 ## 90 BOS SF 256.0000 ## 91 BRO SF 590.6667 ## 92 CHA SF 362.5000 ## 93 CHI SF 379.6667 ## 94 CLE SF 592.7500 ## 95 DAL SF 492.7500 ## 96 DEN SF 909.5000 ## 97 DET SF 317.7500 ## 98 GSW SF 874.0000 ## 99 HOU SF 549.5000 ## 100 IND SF 353.5000 ## 101 LAC SF 498.0000 ## 102 LAL SF 445.3333 ## 103 MEM SF 1168.0000 ## 104 MIA SF 502.3333 ## 105 MIL SF 681.5000 ## 106 MIN SF 389.0000 ## 107 NOR SF 209.5000 ## 108 NYK SF 443.2000 ## 109 OKL SF 354.0000 ## 110 ORL SF 661.0000 ## 111 PHI SF 517.7500 ## 112 PHO SF 479.0000 ## 113 POR SF 374.6667 ## 114 SAC SF 1012.5000 ## 115 SAN SF 547.0000 ## 116 TOR SF 408.5000 ## 117 UTA SF 428.4000 ## 118 WAS SF 401.0000 ## 119 ATL SG 428.5000 ## 120 BOS SG 442.0000 ## 121 BRO SG 516.5000 ## 122 CHA SG 475.0000 ## 123 CHI SG 609.0000 ## 124 CLE SG 443.2500 ## 125 DAL SG 1513.0000 ## 126 DEN SG 216.0000 ## 127 DET SG 853.0000 ## 128 GSW SG 514.1667 ## 129 HOU SG 735.3333 ## 130 IND SG 942.0000 ## 131 LAC SG 371.0000 ## 132 LAL SG 420.2500 ## 133 MEM SG 407.4000 ## 134 MIA SG 500.3333 ## 135 MIL SG 784.6667 ## 136 MIN SG 730.0000 ## 137 NOR SG 722.6667 ## 138 NYK SG 447.0000 ## 139 OKL SG 563.2500 ## 140 ORL SG 536.4000 ## 141 PHI SG 326.2500 ## 142 PHO SG 314.6000 ## 143 POR SG 645.7500 ## 144 SAC SG 657.5000 ## 145 SAN SG 581.5000 ## 146 TOR SG 830.6667 ## 147 UTA SG 270.0000 ## 148 WAS SG 393.0000
類似apply家族的函數：aggregate()-2
aggregate(formula, data=資料, FUN=函數功能)
Formula: if you need all, put [.] into the formula (.~Team+Position)
aggregate(TotalPoints ~ Team+Position,
data = NBA1415, mean)
## Team Position TotalPoints ## 1 BOS C 744.5000 ## 2 BRO C 687.0000 ## 3 CHA C 618.6667 ## 4 CHI C 255.0000 ## 5 CLE C 271.5000 ## 6 DAL C 352.3333 ## 7 DEN C 489.0000 ## 8 DET C 608.5000 ## 9 GSW C 214.3333 ## 10 HOU C 339.0000 ## 11 IND C 532.5000 ## 12 LAC C 946.0000 ## 13 LAL C 307.0000 ## 14 MEM C 916.0000 ## 15 MIA C 442.0000 ## 16 MIL C 391.7500 ## 17 MIN C 264.6000 ## 18 NOR C 366.0000 ## 19 NYK C 388.0000 ## 20 OKL C 850.0000 ## 21 ORL C 822.0000 ## 22 PHI C 346.0000 ## 23 PHO C 432.0000 ## 24 POR C 425.0000 ## 25 SAC C 520.0000 ## 26 TOR C 257.7500 ## 27 UTA C 686.0000 ## 28 WAS C 525.6667 ## 29 ATL PF 592.0000 ## 30 BOS PF 527.0000 ## 31 BRO PF 280.2000 ## 32 CHA PF 288.3333 ## 33 CHI PF 732.7500 ## 34 CLE PF 964.5000 ## 35 DAL PF 624.0000 ## 36 DEN PF 473.6667 ## 37 DET PF 536.3333 ## 38 GSW PF 589.5000 ## 39 HOU PF 613.0000 ## 40 IND PF 476.0000 ## 41 LAC PF 554.2500 ## 42 LAL PF 507.6667 ## 43 MEM PF 310.4000 ## 44 MIA PF 368.2500 ## 45 MIL PF 263.0000 ## 46 MIN PF 184.2000 ## 47 NOR PF 943.0000 ## 48 NYK PF 259.0000 ## 49 OKL PF 463.6667 ## 50 ORL PF 320.0000 ## 51 PHI PF 265.5000 ## 52 PHO PF 669.7500 ## 53 POR PF 842.5000 ## 54 SAC PF 298.1667 ## 55 SAN PF 511.3333 ## 56 TOR PF 541.5000 ## 57 UTA PF 442.5000 ## 58 WAS PF 507.6667 ## 59 ATL PG 743.0000 ## 60 BOS PG 718.0000 ## 61 BRO PG 641.3333 ## 62 CHA PG 640.0000 ## 63 CHI PG 929.0000 ## 64 CLE PG 818.5000 ## 65 DAL PG 490.2500 ## 66 DEN PG 604.0000 ## 67 DET PG 498.0000 ## 68 GSW PG 1180.5000 ## 69 HOU PG 421.0000 ## 70 IND PG 637.7500 ## 71 LAC PG 497.5000 ## 72 LAL PG 452.0000 ## 73 MEM PG 435.5000 ## 74 MIA PG 364.6667 ## 75 MIL PG 371.2000 ## 76 MIN PG 417.2500 ## 77 NOR PG 240.3333 ## 78 NYK PG 454.7500 ## 79 OKL PG 931.3333 ## 80 ORL PG 459.5000 ## 81 PHI PG 272.0000 ## 82 PHO PG 644.5000 ## 83 POR PG 709.0000 ## 84 SAC PG 397.7500 ## 85 SAN PG 620.6667 ## 86 TOR PG 1009.5000 ## 87 UTA PG 475.6667 ## 88 WAS PG 603.0000 ## 89 ATL SF 506.0000 ## 90 BOS SF 256.0000 ## 91 BRO SF 590.6667 ## 92 CHA SF 362.5000 ## 93 CHI SF 379.6667 ## 94 CLE SF 592.7500 ## 95 DAL SF 492.7500 ## 96 DEN SF 909.5000 ## 97 DET SF 317.7500 ## 98 GSW SF 874.0000 ## 99 HOU SF 549.5000 ## 100 IND SF 353.5000 ## 101 LAC SF 498.0000 ## 102 LAL SF 445.3333 ## 103 MEM SF 1168.0000 ## 104 MIA SF 502.3333 ## 105 MIL SF 681.5000 ## 106 MIN SF 389.0000 ## 107 NOR SF 209.5000 ## 108 NYK SF 443.2000 ## 109 OKL SF 354.0000 ## 110 ORL SF 661.0000 ## 111 PHI SF 517.7500 ## 112 PHO SF 479.0000 ## 113 POR SF 374.6667 ## 114 SAC SF 1012.5000 ## 115 SAN SF 547.0000 ## 116 TOR SF 408.5000 ## 117 UTA SF 428.4000 ## 118 WAS SF 401.0000 ## 119 ATL SG 428.5000 ## 120 BOS SG 442.0000 ## 121 BRO SG 516.5000 ## 122 CHA SG 475.0000 ## 123 CHI SG 609.0000 ## 124 CLE SG 443.2500 ## 125 DAL SG 1513.0000 ## 126 DEN SG 216.0000 ## 127 DET SG 853.0000 ## 128 GSW SG 514.1667 ## 129 HOU SG 735.3333 ## 130 IND SG 942.0000 ## 131 LAC SG 371.0000 ## 132 LAL SG 420.2500 ## 133 MEM SG 407.4000 ## 134 MIA SG 500.3333 ## 135 MIL SG 784.6667 ## 136 MIN SG 730.0000 ## 137 NOR SG 722.6667 ## 138 NYK SG 447.0000 ## 139 OKL SG 563.2500 ## 140 ORL SG 536.4000 ## 141 PHI SG 326.2500 ## 142 PHO SG 314.6000 ## 143 POR SG 645.7500 ## 144 SAC SG 657.5000 ## 145 SAN SG 581.5000 ## 146 TOR SG 830.6667 ## 147 UTA SG 270.0000 ## 148 WAS SG 393.0000
Missing Value
計算平均的時候，該不該跳過缺值
x<-c(1,2,3,4,5,NA) mean(x)
## [1] NA
mean(x, na.rm=T)
## [1] 3
sum(x)
## [1] NA
sum(x, na.rm=T)
## [1] 15
Removing NA
Subset
x <- c(1, 2, NA, 4, NA, 5) x[! is.na(x)]
## [1] 1 2 4 5
complete.cases(x) #可使用在data frame，取出所有欄位都不是NA的row
## [1] TRUE TRUE FALSE TRUE FALSE TRUE
問題？？？？？
R Markdown
R Markdown
- http://rmarkdown.rstudio.com/
- R + Markdown
- R Markdown—knitr—>Markdown
- Markdown—markdown—>HTML
- 用來撰寫可以重複執行/動態的 R語言執行報告
- 報告格式：Slides, PDF, HTML. Word,…
- 又要交作業了….
R Markdown 元件
- 基於Markdown文件
- 所有寫在R Code Chunks的R程式碼都會執行，並將結果輸出
- 放上GitHub的文件，會輸出成.md檔
Step 1 開啟新的R Markdown 檔案
如果跳出是否要安裝Packages，選要
Step 2 設定輸出格式
最後要放上GitHub，所以選From Template裡的GitHub Document
Step 3 設定文件基本資訊
程式會產生內建的參考範例，先按“Knit”試試
自動生成的.md檔案在GitHub上的樣子
R Code Chunk的參數
- eval：要不要執行並顯示在輸出的Markdown檔案內
- echo：要不要一起顯示程式碼以及執行結果
- warning, error, message：要不要顯示錯誤訊息
- cache：是否要暫存結果
Homework 3
分析NBA 2014到2015球季
- 各隊最辛苦的球員（出戰分鐘數TotalMinutesPlayed 最多）
- 各隊得分王（TotalPoints最多）
- 各隊最有效率的球員（總得分/出戰分鐘數 最高）
- 各隊三分球出手最準的球員（ThreesMade/ThreesAttempted 最高）
Homework 3 詳細要求與配分1
- Title: NBA 2014-2015球季 各隊分析 (5 pt)
- 把資料讀進來的程式碼 (5 pt)
- 次標題1：各隊最辛苦的球員 (20 pt)
- 標題 (2)，說明 (3)，程式碼 (5) 和結果 (10)
- 結果要包括隊名 (2) 與球員姓名 (3) 與總出場數 (3)，按照總出場數排序(大到小) (2)
Homework 3 詳細要求與配分2
- 次標題2：各隊得分王 (20 pt)
- 程式碼和結果，輸出要包括隊名與球員姓名與總分，按照總分排序
- 次標題3：各隊最有效率的球員 (25 pt)
- 程式碼和結果，輸出要包括隊名與球員姓名與效率計算結果 (8)，按照效率計算結果排序
- 次標題4：各隊三分球出手最準的球員 (25 pt)
- 程式碼和結果，輸出要包括隊名與球員姓名與三分球命中率 (8)，按照三分球命中率排序
作業範例 — R Markdown
作業範例 — Markdown@GitHub
作業繳交
所有作業除了有特別寫，遲交一天扣5分，最多扣40分（8天）
URL範例： https://github.com/yijutseng/BigDataCGUIM/blob/master/HW3.md
版本碼是指BigDataCGUIM的SHA碼 
問題？？？？？？？？？？？
R Function
函數 Function
- 將程式碼依功能要求，寫成可重複使用的『函數』
- mean()
- sum()
- 好處：
- 程式碼變短，閱讀和偵錯容易
- 功能可以重複使用，節省開發時間
定義一個新的函數
函數名稱<-function(參數1, 參數2, ….)({程式碼們}
四捨五入到小數點第二位
round2<-function(vector){
round(vector,digits = 2)
}
round2(3.886)
## [1] 3.89
Lazy Evaluation
沒用到Nothing參數，程式也不會出錯
round2Lazy<-function(vector,nothing){
round(vector,digits = 2)
}
round2(3.886)
## [1] 3.89
Lazy Evaluation 2
- 使用f參數時，沒有輸入b，但R也不會檢查
- 在執行print(b)時才會跳出錯誤訊息
f <- function(a, b) {
print(a)
print(b)
}
f(45)
## [1] 45
## Error in print(b): argument "b" is missing, with no default
…參數
- 意指引用其他函數的參數
- apply(Data, MARGIN, FUN,…)
- 例：平均再取四捨五入
roundmean<-function(vector, ...){
round(mean(vector, ...),digits=2)
}
roundmean(c(1.1,2,3,4,5))
## [1] 3.02
roundmean(c(1.1,2,3,4,5,NA))
## [1] NA
roundmean(c(1.1,2,3,4,5,NA),na.rm=T)
## [1] 3.02
…參數 2
roundmean<-function(vector, ...){
round(mean(vector, ...),digits=2)
}
roundmean(c(1.1,2,3,4,5))
## [1] 3.02
roundmean(c(1.1,2,3,4,5,NA))
## [1] NA
roundmean(c(1.1,2,3,4,5,NA),na.rm=T)
## [1] 3.02
參數預設值
使用者如果沒有指定參數值，直接帶入預設
roundDe<-function(v=1.111:10.111){
round(v,digits = 2)
}
roundDe(1.66:6.66)
## [1] 1.66 2.66 3.66 4.66 5.66 6.66
roundDe()
## [1] 1.11 2.11 3.11 4.11 5.11 6.11 7.11 8.11 9.11 10.11
retrun()
停止函數執行，並回傳值
round2<-function(v){
if(!is.numeric(v)){
print("輸入數字")
return()
}
round(v,digits = 2)
}
round2("a")
## [1] "輸入數字"
## NULL
函數也可以當作參數來用
apply(Data, MARGIN, FUN,…)
apply(iris,2,max)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## "7.9" "4.4" "6.9" "2.5" "virginica"
RoundNumber2<-function(v,XFun){
round(XFun(v),digits = 2)
}
RoundNumber2(1.1:10.1,mean)
## [1] 5.6
問題？？？？？？？？？？？
Getting Data
What is 'Data'
http://en.wikipedia.org/wiki/Data
Data are values of qualitative or quantitative variables, belonging to a set of items.
Getting Data
學會：
- 找資料、抓資料、總之就是弄到資料
- 資料前處理原則：tidy
- 用R做到以上工作
Raw data -> Processing script -> Tidy data -> Data analysis -> Data communication
課堂上玩得很開心的資料 Tidy Data
- 一個欄位（Column）內只有一個數值，最好要有凡人看得懂的Column Name
- 不同的觀察值應該要在不同行（Raw）
- 一張表裡面，有所有分析需要的資料
- 如果一定要多張表，中間一定要有index可以把表串起來
- One file, one table
NBA1415
## League Name Team Position GamesPlayed TotalMinutesPlayed ## 1 NBA Quincy Acy NYK SF 68 1288 ## 2 NBA Jordan Adams MEM SG 30 249 ## 3 NBA Steven Adams OKL C 70 1776 ## 4 NBA Jeff Adrien MIN PF 17 215 ## 5 NBA Arron Afflalo POR SG 78 2502 ## 6 NBA Alexis Ajinca NOR C 68 956 ## 7 NBA Furkan Aldemir PHI PF 41 541 ## 8 NBA Cole Aldrich NYK C 61 979 ## 9 NBA Lamarcu Aldridge POR PF 71 2514 ## 10 NBA Lavoy Allen IND PF 63 1066 ## 11 NBA Tony Allen MEM SG 63 1647 ## 12 NBA Al-farouq Aminu DAL SF 74 1368 ## 13 NBA Lou Amundson NYK PF 53 937 ## 14 NBA Chris Andersen MIA C 60 1131 ## 15 NBA Alan Anderson BRO SG 74 1743 ## 16 NBA Kyle Anderson SAN SG 33 355 ## 17 NBA Ryan Anderson NOR PF 61 1677 ## 18 NBA G Antetokounmpo MIL SG 81 2542 ## 19 NBA Carmelo Anthony NYK SF 40 1430 ## 20 NBA Joel Anthony DET C 49 408 ## 21 NBA Pero Antic ATL PF 63 1041 ## 22 NBA Trevor Ariza HOU SG 82 2936 ## 23 NBA Darrell Arthur DEN PF 58 986 ## 24 NBA Omer Asik NOR C 77 1979 ## 25 NBA D.j. Augustin OKL PG 82 1964 ## 26 NBA Jeff Ayres SAN PF 51 386 ## 27 NBA Luke Babbitt NOR SF 63 828 ## 28 NBA Cameron Bairstow CHI PF 18 67 ## 29 NBA Leandro Barbosa GSW SG 66 983 ## 30 NBA J.j. Barea DAL PG 77 1368 ## 31 NBA Andrea Bargnani NYK C 29 785 ## 32 NBA Harrison Barnes GSW SF 82 2317 ## 33 NBA Matt Barnes LAC SF 76 2276 ## 34 NBA Earl Barron PHO PF 16 143 ## 35 NBA Will Barton DEN SG 58 982 ## 36 NBA Brandon Bass BOS PF 82 1932 ## 37 NBA Nicolas Batum POR SF 71 2380 ## 38 NBA Jerryd Bayless MIL PG 77 1723 ## 39 NBA Aron Baynes SAN PF 70 1126 ## 40 NBA Kent Bazemore ATL SG 75 1327 ## 41 NBA Bradley Beal WAS SG 63 2109 ## 42 NBA Michael Beasley MIA PF 24 503 ## 43 NBA Marco Belinelli SAN SG 62 1386 ## 44 NBA Jerrelle Benimon UTA PF 2 3 ## 45 NBA Anthony Bennett MIN PF 57 898 ## 46 NBA Patrick Beverley HOU PG 56 1729 ## 47 NBA Sim Bhullar SAC C 3 3 ## 48 NBA Bismack Biyombo CHA C 64 1240 ## 49 NBA Tarik Black LAL PF 63 1195 ## 50 NBA Dejuan Blair WAS C 29 179 ## 51 NBA Steve Blake POR PG 81 1529 ## 52 NBA Eric Bledsoe PHO PG 81 2799 ## 53 NBA Vander Blue LAL SG 2 74 ## 54 NBA Bojan Bogdanovic BRO SF 78 1856 ## 55 NBA Andrew Bogut GSW C 67 1583 ## 56 NBA Matt Bonner SAN PF 72 938 ## 57 NBA Trevor Booker UTA PF 79 1562 ## 58 NBA Carlos Boozer LAL PF 71 1692 ## 59 NBA Chris Bosh MIA PF 44 1557 ## 60 NBA Avery Bradley BOS PG 77 2427 ## 61 NBA Elton Brand ATL PF 36 487 ## 62 NBA Corey Brewer HOU SF 80 2088 ## 63 NBA Aaron Brooks CHI PG 82 1885 ## 64 NBA Jabari Brown LAL SG 19 567 ## 65 NBA Lorenzo Brown MIN PG 29 550 ## 66 NBA Markel Brown BRO SG 47 784 ## 67 NBA Shannon Brown MIA PG 5 89 ## 68 NBA Kobe Bryant LAL SG 35 1206 ## 69 NBA Chase Budinger MIN SF 67 1287 ## 70 NBA Reggie Bullock PHO SG 36 339 ## 71 NBA Trey Burke UTA PG 76 2286 ## 72 NBA Alec Burks UTA PG 27 899 ## 73 NBA Caron Butler DET SF 78 1626 ## 74 NBA Jimmy Butler CHI SG 65 2515 ## 75 NBA Rasual Butler WAS SF 75 1509 ## 76 NBA Dwight Buycks LAL PG 6 124 ## 77 NBA Will Bynum WAS PG 7 67 ## 78 NBA Bruno Caboclo TOR SF 8 24 ## 79 NBA Nick Calathes MEM SG 58 835 ## 80 NBA Jose Calderon NYK PG 42 1269 ## 81 NBA Ke Caldwell-pope DET SG 82 2591 ## 82 NBA Isaiah Canaan PHI PG 48 940 ## 83 NBA Clint Capela HOU C 12 90 ## 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110 NBA Jared Cunningham LAC SG 19 91 ## 111 NBA Seth Curry PHO PG 2 8 ## 112 NBA Stephen Curry GSW PG 80 2613 ## 113 NBA Samuel Dalembert NYK C 32 544 ## 114 NBA Troy Daniels CHA SG 47 401 ## 115 NBA Luigi Datome BOS SF 21 211 ## 116 NBA Brandon Davies BRO PF 27 424 ## 117 NBA Anthony Davis NOR PF 68 2460 ## 118 NBA Ed Davis LAL PF 79 1842 ## 119 NBA Glen Davis LAC PF 74 909 ## 120 NBA Andre Dawkins MIA SG 4 22 ## 121 NBA Austin Daye ATL SF 35 348 ## 122 NBA Dewayne Dedmon ORL C 59 849 ## 123 NBA Matt Dellavedova CLE SG 67 1378 ## 124 NBA Luol Deng MIA SF 72 2420 ## 125 NBA Demar Derozan TOR SG 60 2102 ## 126 NBA Boris Diaw SAN PF 81 1987 ## 127 NBA Gorgui Dieng MIN C 73 2193 ## 128 NBA Spence Dinwiddie DET PG 34 458 ## 129 NBA Joey Dorsey HOU PF 69 856 ## 130 NBA Toney Douglas NOR PG 12 177 ## 131 NBA Douglas-roberts LAC SG 12 103 ## 132 NBA Goran Dragic MIA SG 78 2641 ## 133 NBA Zoran Dragic MIA SG 16 76 ## 134 NBA Larry Drew PHI PG 12 220 ## 135 NBA Andre Drummond DET C 82 2497 ## 136 NBA Jared Dudley MIL SG 72 1716 ## 137 NBA Tim Duncan SAN PF 77 2224 ## 138 NBA Mike Dunleavy CHI SF 63 1837 ## 139 NBA Kevin Durant OKL SF 27 914 ## 140 NBA Cleanthony Early NYK SF 39 651 ## 141 NBA Wayne Ellington LAL SG 65 1676 ## 142 NBA Monta Ellis DAL SG 80 2698 ## 143 NBA James Ennis MIA SF 62 1052 ## 144 NBA Tyler Ennis MIL PG 33 413 ## 145 NBA Jeremy Evans UTA SF 38 266 ## 146 NBA Reggie Evans SAC PF 47 767 ## 147 NBA Tyreke Evans NOR SG 79 2695 ## 148 NBA Dante Exum UTA SG 82 1821 ## 149 NBA Festus Ezeli GSW C 46 504 ## 150 NBA Kenneth Faried DEN PF 75 2085 ## 151 NBA Jordan Farmar LAC PG 36 528 ## 152 NBA Derrick Favors UTA PF 74 2281 ## 153 NBA Raymond Felton DAL PG 29 281 ## 154 NBA Landry Fields TOR SG 26 217 ## 155 NBA Evan Fournier ORL SG 58 1659 ## 156 NBA Randy Foye DEN SG 51 1089 ## 157 NBA Jamaal Franklin DEN SG 3 14 ## 158 NBA Tim Frazier POR PG 11 241 ## 159 NBA Jimmer Fredette NOR PG 50 514 ## 160 NBA Joel Freeland POR C 48 618 ## 161 NBA Channing Frye ORL PF 75 1867 ## 162 NBA Danilo Gallinari DEN SF 59 1421 ## 163 NBA Langsto Galloway NYK PG 45 1459 ## 164 NBA Francisco Garcia HOU SG 14 201 ## 165 NBA Kevin Garnett MIN PF 47 948 ## 166 NBA Marc Gasol MEM C 81 2690 ## 167 NBA Pau Gasol CHI PF 78 2682 ## 168 NBA Rudy Gay SAC SF 68 2412 ## 169 NBA Alonzo Gee POR SF 54 664 ## 170 NBA Paul George IND SF 6 92 ## 171 NBA Taj Gibson CHI PF 62 1694 ## 172 NBA Manu Ginobili SAN SG 70 1591 ## 173 NBA Rudy Gobert UTA C 82 2163 ## 174 NBA Drew Gooden WAS PF 51 866 ## 175 NBA Archie Goodwin PHO SG 41 535 ## 176 NBA Aaron Gordon ORL PF 47 797 ## 177 NBA Ben Gordon ORL SG 56 789 ## 178 NBA Drew Gordon PHI PF 9 72 ## 179 NBA Eric Gordon NOR SG 61 2020 ## 180 NBA Marcin Gortat WAS C 82 2455 ## 181 NBA Danny Granger MIA SF 31 611 ## 182 NBA Jerami Grant PHI SF 65 1380 ## 183 NBA Danny Green SAN SG 81 2311 ## 184 NBA Draymond Green GSW SF 79 2487 ## 185 NBA Erick Green DEN PG 43 414 ## 186 NBA Gerald Green PHO SG 74 1447 ## 187 NBA Jamychal Green MEM PF 24 165 ## 188 NBA Jeff Green MEM SF 78 2459 ## 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真實世界裡的資料 Raw Data: Html
真實世界裡的資料 Raw Data: Facebook
真實世界裡的資料 Raw Data: ?!
資料到底在哪裡？
- 硬碟裡
- Open Data
- 網頁裡（爬蟲！）
- 任何地方
Open Data Example
Open Data Example
Reading Data
讀檔的基本函數們：
read.table,read.csv, for reading tabular datareadLines, for reading lines of a text filesource, for reading in R code files (inverseofdump)dget, for reading in R code files (inverseofdput)load, for reading in saved workspaces
Writing Data
There are analogous functions for writing data to files
- write.table
- writeLines
- dump
- dput
- save
- serialize
Reading Data Files with read.table
The read.table function is one of the most commonly used functions for reading data. It has a few important arguments: - file, the name of a file, or a connection - header, logical indicating if the file has a header line - sep, a string indicating how the columns are separated - colClasses, a character vector indicating the class of each column in the dataset - nrows, the number of rows in the dataset - comment.char, a character string indicating the comment character - skip, the number of lines to skip from the beginning - stringsAsFactors, should character variables be coded as factors?
read.table
For small to moderately sized datasets, you can usually call read.table without specifying any other arguments
data <- read.table("foo.txt")
R will automatically
- skip lines that begin with a #
- figure out how many rows there are (and how much memory needs to be allocated)
- figure what type of variable is in each column of the table Telling R all these things directly makes R run faster and more efficiently.
read.csvis identical to read.table except that the default separator is a comma.
Reading in Larger Datasets with read.table
With much larger datasets, doing the following things will make your life easier and will prevent R from choking.
- Read the help page for read.table, which contains many hints
- Make a rough calculation of the memory required to store your dataset. If the dataset is larger than the amount of RAM on your computer, you can probably stop right here.
- Set
comment.char = ""if there are no commented lines in your file.
Reading in Larger Datasets with read.table
- Use the
colClassesargument. Specifying this option instead of using the default can make ’read.table’ run MUCH faster, often twice as fast. In order to use this option, you have to know the class of each column in your data frame. If all of the columns are “numeric”, for example, then you can just setcolClasses = "numeric". A quick an dirty way to figure out the classes of each column is the following:
initial <- read.table("datatable.txt", nrows = 100)
classes <- sapply(initial, class)
tabAll <- read.table("datatable.txt",
colClasses = classes)
- Set
nrows. This doesn’t make R run faster but it helps with memory usage. A mild overestimate is okay. You can use the Unix toolwcto calculate the number of lines in a file.
Know Thy System
In general, when using R with larger datasets, it’s useful to know a few things about your system.
- How much memory is available?
- What other applications are in use?
- Are there other users logged into the same system?
- What operating system?
- Is the OS 32 or 64 bit?
Calculating Memory Requirements
I have a data frame with 1,500,000 rows and 120 columns, all of which are numeric data. Roughly, how much memory is required to store this data frame?
1,500,000 × 120 × 8 bytes/numeric
= 1440000000 bytes
= 1440000000 / \(2^{20}\) bytes/MB
= 1,373.29 MB
= 1.34 GB
Textual Formats
dumpingand dputing are useful because the resulting textual format is edit-able, and in the case of corruption, potentially recoverable.Unlikewriting out a table or csv file,dumpanddputpreserve the metadata (sacrificing some readability), so that another user doesn’t have to specify it all over again.Textualformats can work much better with version control programs like subversion or git which can only track changes meaningfully in text files- Textual formats can be longer-lived; if there is corruption somewhere in the file, it can be easier to fix the problem
- Textual formats adhere to the “Unix philosophy”
- Downside: The format is not very space-efficient
dput-ting R Objects
Another way to pass data around is by deparsing the R object with dput and reading it back in using dget.
> y <- data.frame(a = 1, b = "a")
> dput(y)
structure(list(a = 1,
b = structure(1L, .Label = "a",
class = "factor")),
.Names = c("a", "b"), row.names = c(NA, -1L),
class = "data.frame")
> dput(y, file = "y.R")
> new.y <- dget("y.R")
> new.y
a b
1 1 a
Dumping R Objects
Multiple objects can be deparsed using the dump function and read back in using source.
> x <- "foo"
> y <- data.frame(a = 1, b = "a")
> dump(c("x", "y"), file = "data.R")
> rm(x, y)
> source("data.R")
> y
a b
1 1 a
> x
[1] "foo"
Interfaces to the Outside World
Data are read in using connection interfaces. Connections can be made to files (most common) or to other more exotic things.
file, opens a connection to a filegzfile, opens a connection to a file compressed with gzipbzfile, opens a connection to a file compressed with bzip2url, opens a connection to a webpage
File Connections
> str(file)
function (description = "", open = "", blocking = TRUE,
encoding = getOption("encoding"))
descriptionis the name of the fileopenis a code indicating- “r” read only
- “w” writing (and initializing a new file)
- “a” appending
- “rb”, “wb”, “ab” reading, writing, or appending in binary mode (Windows)
Connections
In general, connections are powerful tools that let you navigate files or other external objects. In practice, we often don’t need to deal with the connection interface directly.
con <- file("foo.txt", "r")
data <- read.csv(con)
close(con)
is the same as
data <- read.csv("foo.txt")
Reading Lines of a Text File
> con <- gzfile("words.gz")
> x <- readLines(con, 10)
> x
[1] "1080" "10-point" "10th" "11-point"
[5] "12-point" "16-point" "18-point" "1st"
[9] "2" "20-point"
writeLines takes a character vector and writes each element one line at a time to a text file.
Reading Lines of a Text File
readLines can be useful for reading in lines of webpages
## This might take time
con <- url("http://www.jhsph.edu", "r")
x <- readLines(con)
> head(x)
[1] "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\">"
[2] ""
[3] "<html>"
[4] "<head>"
[5] "\t<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8